End-to-End Grey Relational AI for Cloud-Based Fraud Analytics SAP-Integrated Risk-Adaptive Threat Mitigation in Healthcare ERP Systems
DOI:
https://doi.org/10.15662/IJARCST.2024.0706026Keywords:
Grey Relational Analysis, AI cloud analytics, Fraud detection, Risk-adaptive threat mitigation, SAP ERP integration, Healthcare ERP, Cybersecurity, Cloud computing, Anomaly detection, Enterprise risk intelligence, Real-time analyticsAbstract
This study presents an end-to-end Grey Relational AI framework for cloud-based fraud analytics, designed to enhance risk detection and adaptive threat mitigation within healthcare ERP systems. Leveraging Grey Relational Analysis (GRA), the framework identifies complex interrelationships among transactional, operational, and behavioral data, enabling precise detection of anomalous patterns indicative of fraud. Integration with SAP ERP allows seamless access to enterprise data and supports real-time analytical processing, while cloud deployment ensures scalability, high availability, and efficient handling of large datasets. Embedded cybersecurity mechanisms—including identity and access management, data encryption, policy-based governance, and continuous threat monitoring—secure sensitive healthcare and financial data. The proposed architecture demonstrates improved fraud detection accuracy, reduced false-positive rates, and enhanced responsiveness to emerging threats. This research contributes a robust, scalable, and secure approach for implementing AI-driven risk intelligence in healthcare ERP systems.References
1. Deng, J. (1989). Introduction to Grey System Theory. Journal of Grey System, 1(1), 1–24.
2. Natarajan, R., Sugumar, R., Mahendran, M., & Anbazhagan, K. (2012). Design a cryptographic approach for privacy preserving data mining. Int. J. Innov. Res. Sci. Eng. Technol, 1(1).
3. Suchitra, R. (2023). Cloud-Native AI model for real-time project risk prediction using transaction analysis and caching strategies. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8006–8013. https://doi.org/10.15662/IJRPETM.2023.0601002
4. Pasumarthi, A., & Joyce, S. SABRIX FOR SAP: A COMPARATIVE ANALYSIS OF ITS FEATURES AND BENEFITS. https://www.researchgate.net/publication/395447894_International_Journal_of_Engineering_Technology_Research_Management_SABRIX_FOR_SAP_A_COMPARATIVE_ANALYSIS_OF_ITS_FEATURES_AND_BENEFITS
5. Navandar, P. (2021). Developing advanced fraud prevention techniques using data analytics and ERP systems. International Journal of Science and Research (IJSR), 10(5), 1326–1329. https://dx.doi.org/10.21275/SR24418104835 https://www.researchgate.net/profile/Pavan-Navandar/publication/386507190_Developing_Advanced_Fraud_Prevention_Techniquesusing_Data_Analytics_and_ERP_Systems/links/675a0ecc138b414414d67c3c/Developing-Advanced-Fraud-Prevention-Techniquesusing-Data-Analytics-and-ERP-Systems.pdf
6. Uddandarao, D. P., & Vadlamani, R. K. (2025). Counterfactual Forecasting of Human Behavior using Generative AI and Causal Graphs. arXiv preprint arXiv:2511.07484.
7. Muthusamy, P., Thangavelu, K., & Bairi, A. R. (2023). AI-Powered Fraud Detection in Financial Services: A Scalable Cloud-Based Approach. Newark Journal of Human-Centric AI and Robotics Interaction, 3, 146-181.
8. Kapadia, V., Jensen, J., McBride, G., Sundaramoothy, J., Deshmukh, R., Sacheti, P., & Althati, C. (2015). U.S. Patent No. 8,965,820. Washington, DC: U.S. Patent and Trademark Office.
9. Vinay Kumar Ch, Srinivas G, Kishor Kumar A, Praveen Kumar K, Vijay Kumar A. (2021). Real-time optical wireless mobile communication with high physical layer reliability Using GRA Method. J Comp Sci Appl Inform Technol. 6(1): 1-7. DOI: 10.15226/2474-9257/6/1/00149
10. HV, M. S., & Kumar, S. S. (2024). Fusion Based Depression Detection through Artificial Intelligence using Electroencephalogram (EEG). Fusion: Practice & Applications, 14(2).
11. Archana, R., & Anand, L. (2023, September). Ensemble Deep Learning Approaches for Liver Tumor Detection and Prediction. In 2023 Third International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS) (pp. 325-330). IEEE.
12. Nagarajan, G. (2022). An integrated cloud and network-aware AI architecture for optimizing project prioritization in healthcare strategic portfolios. International Journal of Research and Applied Innovations, 5(1), 6444–6450. https://doi.org/10.15662/IJRAI.2022.0501004
13. Pourhabibi, T., Peymani, A., & Tavana, M. (2020). Fraud detection: A systematic literature review of graph-based anomaly detection approaches. Decision Support Systems, 133, Article 113303.
14. Jiang, J., Li, Y., He, B., Hooi, B., Chen, J., & Kok Zhi Kang, J. (2022). Spade: A Real-Time Fraud Detection Framework on Evolving Graphs. arXiv preprint arXiv:2211.06977. arXiv
15. Sivaraju, P. S. (2022). Enterprise-Scale Data Center Migration and Consolidation: Private Bank's Strategic Transition to HP Infrastructure. International Journal of Computer Technology and Electronics Communication, 5(6), 6123-6134.
16. Pasumarthi, A., & Joyce, S. SABRIX FOR SAP: A COMPARATIVE ANALYSIS OF ITS FEATURES AND BENEFITS. https://www.researchgate.net/publication/395447894_International_Journal_of_Engineering_Technology_Research_Management_SABRIX_FOR_SAP_A_COMPARATIVE_ANALYSIS_OF_ITS_FEATURES_AND_BENEFITS
17. Vijayaboopathy, V., Yakkanti, B., & Surampudi, Y. (2023). Agile-driven Quality Assurance Framework using ScalaTest and JUnit for Scalable Big Data Applications. Los Angeles Journal of Intelligent Systems and Pattern Recognition, 3, 245-285.
18. Zubair, K. M., Akash, T. R., & Chowdhury, S. A. (2023). Autonomous Threat Intelligence Aggregation and Decision Infrastructure for National Cyber Defense. Frontiers in Computer Science and Artificial Intelligence, 2(2), 26-51.
19. Mani, R. (2022). Enhancing SAP HANA Resilience and Performance on RHEL using Pacemaker: A Strategic Approach to Migration Optimization and Dual-Function Infrastructure Design. International Journal of Computer Technology and Electronics Communication, 5(6), 6061-6074.
20. Liu, J., Gunasekaran, A., & Mahmoudi, A. (2022). Multi-sourcing and Supplier Classification through Dynamic Grey Relational Analysis Method. Computers & Industrial Engineering.
21. S. Roy and S. Saravana Kumar, “Feature Construction Through Inductive Transfer Learning in Computer Vision,” in Cybernetics, Cognition and Machine Learning Applications: Proceedings of ICCCMLA 2020, Springer, 2021, pp. 95–107.
22. Kapadia, V., Jensen, J., McBride, G., Sundaramoothy, J., Deshmukh, R., Sacheti, P., & Althati, C. (2015). U.S. Patent No. 8,965,820. Washington, DC: U.S. Patent and Trademark Office.
23. Md, A. R. (2023). Machine learning–enhanced predictive marketing analytics for optimizing customer engagement and sales forecasting. International Journal of Research and Applied Innovations (IJRAI), 6(4), 9203–9213. https://doi.org/10.15662/IJRAI.2023.0604004
24. Kanumarlapudi, P. K., Peram, S. R., & Kakulavaram, S. R. (2024). Evaluating Cyber Security Solutions through the GRA Approach: A Comparative Study of Antivirus Applications. International Journal of Computer Engineering and Technology (IJCET), 15(4), 1021-1040.
25. Archana, R., & Anand, L. (2023, May). Effective Methods to Detect Liver Cancer Using CNN and Deep Learning Algorithms. In 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (pp. 1-7). IEEE.
26. Kandula N (2023). Gray Relational Analysis of Tuberculosis Drug Interactions A Multi-Parameter Evaluation of Treatment Efficacy. J Comp Sci Appl Inform Technol. 8(2): 1-10.
27. Chen, H., & Hao, L. (2021). Real-Time Anomaly Detection in Payment Networks: A Machine Learning Approach. Journal of Financial Crime, 28(3), 789–804.
28. Vinay Kumar Ch, Srinivas G, Kishor Kumar A, Praveen Kumar K, Vijay Kumar A. (2021). Real-time optical wireless mobile communication with high physical layer reliability Using GRA Method. J Comp Sci Appl Inform Technol. 6(1): 1-7. DOI: 10.15226/2474-9257/6/1/00149
29. Kumar, R. K. (2022). AI-driven secure cloud workspaces for strengthening coordination and safety compliance in distributed project teams. International Journal of Research and Applied Innovations (IJRAI), 5(6), 8075–8084. https://doi.org/10.15662/IJRAI.2022.0506017
30. Inampudi, R. K., Pichaimani, T., & Kondaveeti, D. (2022). Machine Learning in Payment Gateway Optimization: Automating Payment Routing and Reducing Transaction Failures in Online Payment Systems. Journal of Artificial Intelligence Research, 2(2), 276-321.
31. Ramakrishna, S. (2022). AI-augmented cloud performance metrics with integrated caching and transaction analytics for superior project monitoring and quality assurance. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(6), 5647–5655. https://doi.org/10.15662/IJEETR.2022.0406005
32. Muthusamy, M. (2022). AI-Enhanced DevSecOps architecture for cloud-native banking secure distributed systems with deep neural networks and automated risk analytics. International Journal of Research Publication and Engineering Technology Management, 6(1), 7807–7813. https://doi.org/10.15662/IJRPETM.2022.0506014
33. Thangavelu, K., Keezhadath, A. A., & Selvaraj, A. (2022). AI-Powered Log Analysis for Proactive Threat Detection in Enterprise Networks. Essex Journal of AI Ethics and Responsible Innovation, 2, 33-66.
34. Natarajan, R., Sugumar, R., Mahendran, M., & Anbazhagan, K. (2012). Design a cryptographic approach for privacy preserving data mining. Int. J. Innov. Res. Sci. Eng. Technol, 1(1). Smith, R., & Patel, K. (2015). Enterprise-Scale Graph Analytics for Fraud Detection. International Journal of Data Intelligence, 2(2), 45–57.
